Parse.ly Analytics VS FuzzyWuzzy

I've also seen parse.ly pop up a bit, I might try it to see if it's any decent. Source: over 1 year ago

Parse.ly | Python Data Engineers (NA) & Machine Learning Engineers (EU) | Remote | Full-Time | https://parse.ly Are you a Python programmer based in North or South America, interested in large-scale data processing (terabytes per month, petabytes in our archive), and making use of massively-parallel computing architectures, such as those behind Spark and Dask? Or, are you a Machine Learning Engineer in Western or... - Source: Hacker News / almost 3 years ago

Would be really useful (not to mention polite) if sources were cited when you do this. For example, I think the early points are from the parse.ly report. People might want to click through for context if you let them. Source: about 3 years ago

Do fuzzy matching (something like fuzzywuzzy maybe) to see if the the words line up (allowing for wrong words). You'll need to work out how to use scoring to work out how well aligned the two lists are. Source: over 1 year ago

Convert the original lines to full furigana and do a fuzzy match. (For reference, the original line is 貴方がこれまでに得てきた力、存分に発揮してくださいね。) You can do a regional search using the initial scene data (E60) first, and if the confidence is low, go for a slower full search. Source: over 1 year ago

It's now known as "thefuzz", see https://github.com/seatgeek/fuzzywuzzy. Source: almost 2 years ago

You can have a look at this library to use fuzzy search instead of looking for plaintext muck: https://github.com/seatgeek/fuzzywuzzy. Source: over 2 years ago

To deal with comparing the string, I found FuzzyWuzzy ratio function that is returning a score of how much the strings are similar from 0-100. Source: almost 3 years ago